store = {}
store['args']={'num_inference_samples': 10, 'available_sample_k': 5, 'seed': 129113, 'type': 'AcquisitionFunction.random', 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST_balanced with random acquisition', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'initial_percentage': 50, 'reduce_percentage': 10, 'min_remaining_percentage': 30, 'min_candidates_per_acquired_item': 100, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 'emnist_balanced_independent_random_k10_b5_129113', 'experiments_laaos': './experiment_configs/emnist_random/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=emnist_balanced_independent_random_k10_b5_129113', '--experiments_laaos=./experiment_configs/emnist_random/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.02122340425531915, 'nll': 3.8682813031749514}, 'chosen_samples': [82735, 67638, 69074, 110686, 19508], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 21.503488736999998, 'batch_acquisition_elapsed_time': 0.01678999799992198})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06122340425531915, 'nll': 52.44255573375681}, 'chosen_samples': [60244, 40258, 95281, 71968, 79284], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.95255417999999, 'batch_acquisition_elapsed_time': 0.0025121810000428013})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.10042553191489362, 'nll': 44.09550845790291}, 'chosen_samples': [37190, 96393, 86030, 44942, 104331], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 46.21300141100005, 'batch_acquisition_elapsed_time': 0.002646473000027072})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.1251595744680851, 'nll': 34.26722396709057}, 'chosen_samples': [103736, 64758, 100933, 393, 75908], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 55.34427585500009, 'batch_acquisition_elapsed_time': 0.0023749000000634624})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.13930851063829788, 'nll': 32.858583327164034}, 'chosen_samples': [107792, 68025, 23153, 392, 82606], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 56.34562591600002, 'batch_acquisition_elapsed_time': 0.0023962410000422096})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.15941489361702127, 'nll': 32.555142251346965}, 'chosen_samples': [21933, 25048, 11324, 50459, 20212], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.97075987599999, 'batch_acquisition_elapsed_time': 0.002764492000096652})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.17909574468085107, 'nll': 31.95380603123725}, 'chosen_samples': [6143, 69729, 26111, 29604, 82455], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.759847203999925, 'batch_acquisition_elapsed_time': 0.0026604600000155187})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.19037234042553192, 'nll': 28.982680993864513}, 'chosen_samples': [59749, 65038, 3254, 108891, 18083], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 70.02619652299995, 'batch_acquisition_elapsed_time': 0.002560950000088269})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.19430851063829788, 'nll': 23.97352546993849}, 'chosen_samples': [18588, 52327, 72763, 9266, 32334], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 45.6723837510001, 'batch_acquisition_elapsed_time': 0.002480353999999352})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21313829787234043, 'nll': 22.706251332731956}, 'chosen_samples': [78596, 80257, 23316, 36754, 23414], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.73226663799983, 'batch_acquisition_elapsed_time': 0.0023605860001225665})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21819148936170213, 'nll': 22.73690147679093}, 'chosen_samples': [5514, 51796, 6226, 82197, 78365], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.404682913000215, 'batch_acquisition_elapsed_time': 0.0027572030001010717})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21925531914893617, 'nll': 19.41992053967334}, 'chosen_samples': [7620, 95574, 43405, 65874, 100545], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.830406192000055, 'batch_acquisition_elapsed_time': 0.002322264999975232})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23175531914893616, 'nll': 20.928436953464082}, 'chosen_samples': [24211, 71319, 48086, 18326, 67479], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.138634622999916, 'batch_acquisition_elapsed_time': 0.0023280969999177614})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.2547340425531915, 'nll': 18.71801662376904}, 'chosen_samples': [29237, 108708, 82150, 43323, 77147], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 54.174874863000014, 'batch_acquisition_elapsed_time': 0.002621862000069086})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.25101063829787235, 'nll': 18.214156349055315}, 'chosen_samples': [90390, 4880, 15645, 47751, 37774], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.06806031400015, 'batch_acquisition_elapsed_time': 0.0024286760001359653})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26904255319148934, 'nll': 15.335830605383247}, 'chosen_samples': [16677, 50607, 16475, 57773, 56360], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.91051206999987, 'batch_acquisition_elapsed_time': 0.0026824199999282428})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.24808510638297873, 'nll': 13.965077807017458}, 'chosen_samples': [10568, 110084, 22014, 55565, 48609], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.72099154300008, 'batch_acquisition_elapsed_time': 0.002797218000068824})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26154255319148934, 'nll': 14.382030120749427}, 'chosen_samples': [86607, 82603, 93658, 91981, 79968], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.785101021999935, 'batch_acquisition_elapsed_time': 0.002854787999922337})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2726595744680851, 'nll': 14.786961303315268}, 'chosen_samples': [51264, 39961, 52765, 54449, 17690], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.00987759899999, 'batch_acquisition_elapsed_time': 0.0025397880001492013})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2827659574468085, 'nll': 14.04459799656589}, 'chosen_samples': [67305, 16938, 71837, 57530, 74767], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.2055474660001, 'batch_acquisition_elapsed_time': 0.002461001000028773})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.29904255319148937, 'nll': 13.875250116930998}, 'chosen_samples': [101921, 58149, 13681, 90804, 53807], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.81980880900005, 'batch_acquisition_elapsed_time': 0.002761700000064593})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.30531914893617024, 'nll': 13.352409751403206}, 'chosen_samples': [68906, 75919, 69893, 47963, 62406], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.56288362500004, 'batch_acquisition_elapsed_time': 0.0024316489998454927})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.301436170212766, 'nll': 13.771634127627664}, 'chosen_samples': [63267, 81974, 37539, 72124, 43783], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.72008053799982, 'batch_acquisition_elapsed_time': 0.0026171289998728753})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3396808510638298, 'nll': 10.884352066005164}, 'chosen_samples': [37943, 112641, 59846, 12709, 19901], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.535236923999946, 'batch_acquisition_elapsed_time': 0.0025507330001346418})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.34122340425531916, 'nll': 9.799556235259518}, 'chosen_samples': [25996, 8424, 5504, 98117, 2212], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.85198105199993, 'batch_acquisition_elapsed_time': 0.002272599999969316})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35079787234042553, 'nll': 9.334161536601949}, 'chosen_samples': [20087, 61035, 15089, 5028, 73920], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.98304840100013, 'batch_acquisition_elapsed_time': 0.00251380000008794})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35393617021276597, 'nll': 9.191656835497058}, 'chosen_samples': [26512, 70340, 34397, 102823, 44335], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.16428056699988, 'batch_acquisition_elapsed_time': 0.002475564999940616})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35898936170212764, 'nll': 9.232821793874212}, 'chosen_samples': [42133, 99620, 87069, 72668, 47406], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.325891961000025, 'batch_acquisition_elapsed_time': 0.0025705069999730767})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3749468085106383, 'nll': 8.888367563705813}, 'chosen_samples': [21721, 6819, 67232, 87192, 66633], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.72069464000015, 'batch_acquisition_elapsed_time': 0.0023942309999256395})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3825531914893617, 'nll': 7.677480110822204}, 'chosen_samples': [41951, 89090, 27791, 2028, 18384], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 35.71386270000016, 'batch_acquisition_elapsed_time': 0.0025811670000166487})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.38409574468085106, 'nll': 8.959406144184634}, 'chosen_samples': [87734, 43158, 464, 377, 105444], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.853514797000116, 'batch_acquisition_elapsed_time': 0.0022869559998071054})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3841489361702128, 'nll': 8.693789268466704}, 'chosen_samples': [79653, 40359, 4499, 33831, 51806], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.193679008000345, 'batch_acquisition_elapsed_time': 0.0031277719999707188})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4013829787234043, 'nll': 7.945782701913978}, 'chosen_samples': [111587, 9923, 110066, 87865, 77970], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.677719312000136, 'batch_acquisition_elapsed_time': 0.0026322889998482424})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.39845744680851064, 'nll': 8.188211175788274}, 'chosen_samples': [15972, 25068, 111426, 26761, 4114], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 35.907865308000055, 'batch_acquisition_elapsed_time': 0.0024805660000311036})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.40606382978723404, 'nll': 7.246715608833951}, 'chosen_samples': [56547, 54737, 33390, 56010, 58696], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.250435344999914, 'batch_acquisition_elapsed_time': 0.0027772439998443588})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.41079787234042553, 'nll': 7.488483442844227}, 'chosen_samples': [69186, 41163, 96585, 82508, 20314], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.490925483999945, 'batch_acquisition_elapsed_time': 0.002737293000336649})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.40569148936170213, 'nll': 7.540084920568987}, 'chosen_samples': [88319, 66908, 56232, 21724, 12014], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.20428697399984, 'batch_acquisition_elapsed_time': 0.002679495000393217})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4123936170212766, 'nll': 7.84806758678752}, 'chosen_samples': [58529, 33079, 38358, 672, 13875], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.311680932999934, 'batch_acquisition_elapsed_time': 0.002456625000377244})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4199468085106383, 'nll': 6.942235803515669}, 'chosen_samples': [85652, 54447, 16580, 48560, 104309], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.11817518299995, 'batch_acquisition_elapsed_time': 0.0025068099998861726})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.41904255319148936, 'nll': 6.959380893135006}, 'chosen_samples': [10633, 22465, 6987, 27641, 35104], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.96846603699987, 'batch_acquisition_elapsed_time': 0.002308199999788485})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4221276595744681, 'nll': 6.04498261078907}, 'chosen_samples': [74595, 89037, 74063, 61730, 37497], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 35.67510693599979, 'batch_acquisition_elapsed_time': 0.002346423999824765})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.41617021276595745, 'nll': 6.125448241379033}, 'chosen_samples': [49195, 56306, 7510, 28484, 32554], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 37.1146347519998, 'batch_acquisition_elapsed_time': 0.0023995199999262695})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.43696808510638296, 'nll': 6.359981025950073}, 'chosen_samples': [44237, 27253, 34917, 82085, 85947], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 35.46740461299987, 'batch_acquisition_elapsed_time': 0.002331857000172022})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4417553191489362, 'nll': 5.636875695086223}, 'chosen_samples': [44723, 102496, 17765, 92323, 28042], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.44531899200001, 'batch_acquisition_elapsed_time': 0.002840209000169125})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.46111702127659576, 'nll': 5.060632383180109}, 'chosen_samples': [21368, 57178, 32089, 74278, 63758], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.62132043600013, 'batch_acquisition_elapsed_time': 0.0024599700000180746})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.45872340425531916, 'nll': 5.399042767322126}, 'chosen_samples': [10131, 29218, 56239, 29162, 37396], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.88692776900007, 'batch_acquisition_elapsed_time': 0.0026695749997998064})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.44882978723404254, 'nll': 5.515277564493901}, 'chosen_samples': [40702, 836, 99719, 74357, 54089], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.1550697749999, 'batch_acquisition_elapsed_time': 0.0023389680000036606})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4548404255319149, 'nll': 5.427109096036631}, 'chosen_samples': [42931, 54300, 44922, 9716, 11849], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 36.134673862, 'batch_acquisition_elapsed_time': 0.0024078449996522977})
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